Data quality in hospital information systems: Lessons learned from analyzing 30 years of patient data in a regional German hospital.

BACKGROUND: The integration of Hospital Information Systems (HIS) into healthcare delivery has significantly enhanced patient care and operational efficiency. Nonetheless, the rapid acceleration of digital transformation has led to a substantial increase in the volume of data managed by these systems. This emphasizes the need for robust mechanisms for data management and quality assurance. OBJECTIVE: This study addresses data quality issues related to patient identifiers within the Hospital Information System (HIS) of a regional German hospital, focusing on improving the accuracy and consistency of these administrative data entries. METHODS: Employing a combination of data analysis and expert interviews, this study reviews and programmatically cleanses a dataset with over 2,000,000 patient data entries extracted from the HIS. The areas of investigation are patient admissions, discharges, and geographical data. RESULTS: The analysis revealed that roughly 25% of the dataset was rendered unusable by errors and inconsistencies. By implementing a thorough data cleansing process, we significantly enhanced the utility of the dataset. In doing so, we identified the primary issues affecting data quality, including ambiguities among similar variables and a gap between the intended and actual use of the system. CONCLUSION: The findings highlight the critical importance of enhancing data quality in healthcare information systems. This study shows the necessity of a careful review of data extracted from the HIS before it can be reliably utilized for machine learning tasks, thereby rendering the data more usable for both clinical and analytical purposes.

[Geriatric assessment goes digital-Possible applications in the emergency department setting]. / Geriatrisches Assessment goes digital ­ Einsatzmöglichkeiten im Setting der Notaufnahme.

The geriatric assessment is a basic requirement and a key quality parameter in geriatric care. An increasing number of older patients are presenting to emergency or central admission departments and discharge units in hospitals. For this reason, and in view of the time-critical decision-making requirements in this setting, digital applications of basic geriatric assessment data are becoming increasingly more important for the high-quality follow-up care of geriatric patients.

Severity of illness and risk of mortality from all patient refined-diagnosis related groups: Two scales of different concepts or two sides of the same coin?

All patient refined-diagnosis related groups (APR-DRGs) includes severity of illness (SOI) and risk of mortality (ROM) subclasses. For predictions, both subscales are used together or interchangeably. We aimed to compare SOI and ROM by evaluating the reliability and agreement between both. We performed a retrospective observational study using mainland Portuguese public hospitalisations of adult patients from 2011 to 2016. Reliability (quadratic weighted kappa) and agreement (proportion of agreement) between SOI and ROM were analysed overall and by APR-DRG. While overall reliability and agreement between SOI and ROM were high (weighted kappa: 0.717, 95% CI 0.717-0.718; proportion of agreement: 69.0%, 95% CI 69.0-69.0) there was high heterogeneity across APR-DRGs, ranging from 0.016 to 0.846 on reliability and from 23.1% to 94.8% on agreement. Most of APR-DRGs (263 out of 284) showed a higher proportion of episodes with ROM level above the SOI level than the opposite. In conclusion, SOI and Risk of Mortality measures must be clearly distinguished and are 'two scales of different concepts' rather than 'two sides of the same coin'. However, this is more evident for some APR-DRGs than for others.

Activity, triage levels and impact of the pandemic on hospital emergency departments: A multicentre cross-sectional study.

AIM: Describe the activity of hospital emergency departments (EDs) and the sociodemographic profile of patients in the eight public hospitals in Spain, according to the different triage levels, and to analyse the impact of the SARS-CoV-2 pandemic on patient flow. DESIGN: An observational, descriptive, cross-sectional and retrospective study was carried out. METHODS: Three high-tech public hospitals and five low-tech hospitals consecutively included 2,332,654 adult patients seen in hospital EDs from January 2018 to December 2021. Hospitals belonging to the Catalan Institute of Health. The main variable was triage level, classified according to a standard for the Spanish structured triage system known as Sistema Español de Triaje. For each of the five triage levels, a negative binomial regression model adjusted for year and hospital was performed. The analysis was performed with the R 4.2.2 software. RESULTS: The mean age was 55.4 years. 51.4% were women. The distribution of patients according to the five triage levels was: level 1, 0.41% (n = 9565); level 2, 6.10% (n = 142,187); level 3, 40.2% (n = 938,203); level 4, 42.6% (n = 994,281); level 5, 10.6% (n = 248,418). The sociodemographic profile was similar in terms of gender and age: as the level of severity decreased, the number of women, mostly young, increased. In the period 2020-2021, the emergency rate increased for levels 1, 2 and 3, but levels 4 and 5 remained stable. CONCLUSION: More than half of the patients attended in high-technology hospital EDs were of low severity. The profile of these patients was that of a young, middle-aged population, mostly female. The SARS-CoV2 pandemic did not change this pattern, but an increase in the level of severity was observed. IMPACT: What problem did the study address? There is overcrowding in hospital EDs. What were the main findings? This study found that more than half of the patients attended in high-technology hospital EDs in Spain have low or very low levels of severity. Young, middle-aged women were more likely to visit EDs with low levels of severity. The SARS-CoV2 pandemic did not change this pattern, but an increase in severity was observed. Where and on whom will the research have an impact? The research will have an impact on the functioning of hospital EDs and their staff. PATIENT OR PUBLIC CONTRIBUTION: Not applicable.

The Impact of Real-Time Documentation of In-Hospital Medication Changes on Preventing Undocumented Discrepancies at Discharge and Improving Physician-Pharmacist Communication: A Retrospective Cohort Study and Survey.

Background: Transitional medication safety is crucial, as miscommunication about medication changes can lead to significant risks. Unclear or incomplete documentation during care transitions can result in outdated or incorrect medication lists at discharge, potentially causing medication errors, adverse drug events, and inadequate patient education. These issues are exacerbated by extended hospital stays and multiple care events, making accurate medication recall challenging at discharge. Objective: Thus, we aimed to investigate how real-time documentation of in-hospital medication changes prevents undocumented medication changes at discharge and improves physician-pharmacist communication. Methods: We conducted a retrospective cohort study in a tertiary hospital. Two pharmacists reviewed medical records of patients admitted to the acute medical unit from April to June 2020. In-hospital medication discrepancies were determined by comparing preadmission and hospitalization medication lists and it was verified whether the physician's intent of medication changes was clarified by documentation. By a documentation rate of medication changes of 100% and <100%, respectively, fully documented (FD) and partially documented (PD) groups were defined. Any undocumented medication changes at discharge were considered a "documentation error at discharge". Pharmacists' survey was conducted to assess the impact of appropriate documentation on the pharmacists. Results: After reviewing 400 medication records, patients were categorized into FD (61.3%) and PD (38.8%) groups. Documentation errors at discharge were significantly higher in the PD than in the FD group. Factors associated with documentation errors at discharge included belonging to the PD group, discharge from a non-hospitalist-managed ward, and having three or more intentional discrepancies. Pharmacists showed favorable attitudes towards physician's documentation. Conclusion: Appropriate documentation of in-hospital medication changes, facilitated by free-text communication, significantly decreased documentation errors at discharge. This analysis underlines the importance of communication between pharmacists and hospitalists in improving patient safety during transitions of care.

During transitions of care, communication failures among healthcare professionals can lead to medication errors. Therefore, effective sharing of information is essential, especially when intentional changes in prescription orders are made. Documenting medication changes facilitates real-time communication, potentially improving medication reconciliation and reducing discrepancies. However, inadequate documentation of medication changes is common in clinical practice. This retrospective cohort study underlines the importance of real-time documentation of in-hospital medication changes. There was a significant reduction in documentation errors at discharge in fully documented group, where real-time documentation of medication changes was more prevalent. Pharmacists showed favorable attitudes toward the physician's real-time documenting of medication changes because it provided valuable information on understanding the physician's intent and improving communication and also saved time for pharmacists. This study concludes that physicians' documentation on medication changes may reduce documentation errors at discharge, meaning that proper documentation of medication changes could enhance patient safety through effective communication.

Hospitalizations in older adults from the Zona da Mata of Minas Gerais, Brazil: data from the Unified Health System, 2016­2018 / Hospitalizações em idosos da Zona da Mata de Minas Gerais, Brasil: dados do Sistema Único de Saúde, 2016­2018

INTRODUCTION: The growing older population increases proportionately the demand for hospital care due to the increase in health problems. OBJECTIVE: To estimate the prevalence and incidence of hospitalizations, and to investigate associated factors in older adults from the Zona da Mata of Minas Gerais, Brazil, between 2016-2018. Secondly, to provide a more comprehensive epidemiological overview of hospitalizations, the following were estimated: monthly hospitalization rate; hospital mortality rate; frequency of hospitalizations according to diagnosis, hospitalizations for conditions sensitive to primary care and in-hospital death; and hospital costs. METHODS: This is an ecological and descriptive-analytic study. Data were obtained from the Brazilian Hospital Information System (SIH/SUS). RESULTS: The prevalence of hospitalizations was 35.1% (31.2% in women and 39.7% in men). The monthly rate of hospitalizations was higher in older men when compared with older women (Rate-Ratio=1.35 [95% CI=1.27-1.43]) and adult men between 40­59 years (Rate Ratio=2.42 [95% CI=2.26-2.58]). The cumulative incidence of hospitalization was 144/1,000 older persons (125/1,000 women and 169/1,000 men). Factors significantly associated with hospitalizations were: male sex (PR=1.52 [95% CI=1.11-2.08]); hospitalization in surgical bed (PR=1.93 [95% CI=1.05-3.56]); absence of death (PR=1.94 [95% CI=1.03-3.65]); and hospital stay ≥15 days (PR=0.71 [95% CI=0.54 0.95]). The cost of hospitalizations was R$ 220,8 million (mean of R$ 201,700/day). CONCLUSÃO: The findings strengthen the need for preventive healthcare for the older population living in the Zona da Mata of Minas Gerais and alert managers to the substantial socioeconomic impact of hospitalizations.

INTRODUÇÃO: O crescente aumento da população idosa faz aumentar proporcionalmente a demanda por cuidados hospitalares devido ao aumento dos problemas de saúde. OBJETIVO: Estimar a prevalência e incidência de hospitalizações, e investigar fatores associados, em idosos da Zona da Mata Mineira, Brasil, entre 2016-2018. Secundariamente, com intuito de fornecer um panorama epidemiológico mais abrangente acerca das hospitalizações, foram estimadas: taxa mensal de hospitalização; taxa de mortalidade hospitalar; frequência de hospitalizações conforme o diagnóstico, internações por condições sensíveis à atenção primária (ICSAP) e óbito hospitalar; e custos hospitalares. MÉTODO: Trata-se de um estudo ecológico e descritivo-analítico. Os dados foram obtidos do Sistema de Informação Hospitalar brasileiro (SIH/SUS). RESULTADOS: A prevalência de hospitalizações em idosos foi de 35,1% (31,2% em mulheres e 39,7% em homens). A taxa mensal de hospitalização foi maior em homens idosos quando comparados com mulheres idosas (Razão-de-Taxas=1,35 [IC 95%=1,27-1,43]) e homens adultos entre 40­59 anos (Razão-de-Taxas=2,42 [IC 95%=2,26-2,58]). A incidência acumulada de hospitalização foi de 144/1.000 idosos (125/1.000 mulheres e 169/1.000 homens). Os fatores significativamente associados com as hospitalizações foram: sexo masculino (RP=1,52 [IC 95%=1,11­2,08]), internação em leito cirúrgico (RP=1,93 [IC 95%=1,05­3,56]), ausência de óbito (RP=1,94 [IC 95%=1,03-3,65]) e permanência hospitalizado ≥15 dias (RP=0,71 [IC 95%=0,54-0,95]). O custo das hospitalizações foi de R$ 220,8 milhões (média de R$ 201,7 mil/dia). CONCLUSÃO: Os resultados reforçam a necessidade de cuidados preventivos à saúde da população idosa da Zona da Mata Mineira e alertam gestores para o substancial impacto socioeconômico gerado pelas hospitalizações.

Understanding the role and impact of electronic health records in labor and delivery nursing practice: A scoping review protocol.

Background: Electronic health records have a significant impact on nursing practice, particularly in specializations such as labor and delivery, or acute care maternity nursing practice. Although primary studies on the use of electronic health records in labor and delivery have been done, no reviews on this topic exist. Moreover, the topic of labor and delivery nurses' organizing work in the electronic health record-enabled context has not been addressed. Objective: To (a) synthesize research on electronic health record use in labor and delivery nursing and (b) map how labor and delivery nursing organizing work is transformed by the electronic health record (as described in the reviewed studies). Methods: The scoping review will be guided by a modified methodology based on selected recommendations from the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews. A comprehensive search will be conducted in the following databases: CINAHL Complete, MEDLINE, Academic Search Complete, Web of Science, Scopus and Dissertations and Theses Abstracts and Indexes. Included sources will be primary research, dissertations, or theses that address the use of electronic health records in labor and delivery nursing practice in countries with high levels of electronic health record adoption. Data extracted from included sources will be analyzed thematically. Further analysis will theorize labor and delivery nurses' organizing work in the context of electronic health record use by utilizing concepts from Davina Allen's Translational Mobilization Theory. Findings will be presented in tabular and descriptive formats. Conclusion: The findings of this review will help understand transformations of nursing practice in the electronic health record-enabled labor and delivery context and identify areas of future research. We will propose an extension of the Translational Mobilization Theory and theorize nurses' organizing work involving the use of the electronic health record.

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit.

BACKGROUND: Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. OBJECTIVE: We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. METHODS: Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. RESULTS: ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. CONCLUSION: ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.

Unscheduled Emergency Department Revisits Within 48 Hours of Discharge.

This study aimed to analyze early revisits (within 48 hours of discharge) in an Emergency Department. Among the 178,295 visits, 11,686 were revisits, resulting in a rate of 6.55% (95%CI 6.43-6.67). A total of 1,410 revisits required hospitalization, and 252 were due to preventable errors (17.87%). These errors were mainly related to an inadequate therapeutic plan at discharge (47.22%), an incomplete diagnostic process (29.37%), and misdiagnoses (13.10%). These findings represent a technology-enabled clinical audit tool. Electronic Healthcare Records have the potential to: provide quality metrics of hospital performance, help to keep revisit rates updated (assessment through a real-time dashboard), and improve clinical management (by transparency initiatives about errors, and a supportive learning environment regarding lessons learned).

Designing a comprehensive minimum dataset for patients with COVID-19 in Iranian hospital information systems.

BACKGROUND: The Minimum Data Set (MDS) plays a vital role in data exchange, collection and quality improvement. In the context of the COVID-19 pandemic, there is a need for a tailored MDS that aligns with the specific information needs of the Iranian community and integrates seamlessly into the country's Hospital Information Systems (HIS). OBJECTIVE: The study aimed to develop a comprehensive MDS for COVID-19 patients in Iran, with objectives to identify essential data elements and integrate the MDS into HIS, enhancing data exchange and supporting decision-making. METHOD: This study employed a comparative-descriptive approach to design COVID-19 patient data elements based on World Health Organisation and Centers for Disease Control and Prevention guidelines. The Delphi technique involved 35 experts in two rounds for checklist refinement. The finalised MDS consisted of 9 main terms and 80 sub-terms, analysed using descriptive statistics and IBM SPSS software. RESULTS: Of 35 experts involved with the study, 69% were male and 31% female, and Health Information Management experts were the majority (34%). The refined MDS for COVID-19 in Iran comprises 50 data elements, while 30 elements were excluded. The MDS includes 8 main terms and 80 sub-terms, with unanimous approval for identity, underlying disease, and treatment sections. CONCLUSION: The customised MDS for COVID-19 patients in Iran addresses data collection challenges and supports effective disease prevention and management. By providing comprehensive and reliable information, the MDS enhances healthcare quality, facilitates timely access to medical records, and fosters integrated health services.